Built by operators who live it. Built with AI that gets it.
Restaurant truth meets AI discipline — the combination the category has been missing.
- Financereconciling Uber Eats47 acts
- Opspacing 4 kitchens18 paces
- Workforceapproved 3 swaps12 swaps
- Purchasingchecking 12 suppliers31 orders
- Revenuerepricing 8 items24 priced
- Qualityscanning station 2142 scans
- Voicehandled 24 calls24 calls
- 00:00:00 · APPROVAL87%Birmingham · Pause bundle promo on Uber EatsMargin risk on promoted SKUs
- 00:00:01 · AUTO91%Glasgow · Throttled Deliveroo · service speedTicket times rising — pacing intervention
- 00:00:02 · AUTO94%Wing Palace · Released 2 KP staff earlyDemand forecast 38% below average, next 2h
- 00:00:03 · AUTO87%Leeds · Margin guard held · combo blockedDiscount would cut margin 4.2% on promoted SKUs
- 00:00:04 · AUTO89%Bristol · Reordered cod for TuesdayProjected depletion before next delivery
The founding story
AIBOS exists because every piece of restaurant technology we've used — across forty years of hospitality and a decade of building AI systems — treats a restaurant as a reporting problem. Tickets go in. Reports come out. The owner is left to hold the disconnected middle together.
That's backwards. A restaurant is a live operating system. It's making decisions every five seconds. The tech should be too.
We started AIBOS because we couldn't buy it. Now we're building it with a small number of pioneer operators who feel the same way.
Jag Brar
Forty years in hospitality. Jag has built multiple franchise restaurant networks from the ground up to multi-site estates across the UK. He's run every job on the floor, survived every downturn in the industry, and watched good operators burn out holding disconnected systems together manually. His single bet on AIBOS: if the tech actually understands a restaurant, the owner can finally go home.
Mickey Labana
A renowned AI leader with a history of shipping production-grade machine intelligence across multiple domains. Mickey brings the discipline of modern AI — retrieval, reasoning, explainability, guardrails — to a category that rarely sees it. He's convinced that the hardest part of AI in restaurants isn't the models; it's earning the trust to let them act.
WebuiltAIBOSbecausetherestaurantswe'veworkedwithandinvestedindeservedbetterthanreportsafterthefact.
Our manifesto
Restaurants don't have a data problem. They have a decision latency problem.
We believe the best restaurant software is invisible. It keeps service balanced while your team focuses on guests and food. It earns autonomy gradually. Every decision it makes, it can explain.
We believe AI in restaurants should replace admin, not people. Cook. Serve. Lead. Those are human jobs and they always will be. Reconciliation, variance analysis, reporting, and routine scheduling aren't.
We believe pricing should reflect what the product actually does. If an AI Finance Controller handles the same job a human would, it should be priced like staff — not per seat, per site, per anything that doesn't mean anything to an operator.
And we believe the operating layer for restaurants has to be built alongside real operators. That's why we started with a pioneer partner program — and why we're taking it slowly.
Work with us
Pioneer partnerships and select engineering roles are the only ways into AIBOS at this stage. If either interests you — say hello.